@inproceedings{daval-frerot-weis-2020-wmd,
title = "{WMD} at {S}em{E}val-2020 Tasks 7 and 11: Assessing Humor and Propaganda Using Unsupervised Data Augmentation",
author = "Daval-Frerot, Guillaume and
Weis, Yannick",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.246/",
doi = "10.18653/v1/2020.semeval-1.246",
pages = "1865--1874",
abstract = "In this work, we combine the state-of-the-art BERT architecture with the semi-supervised learning technique UDA in order to exploit unlabeled raw data to assess humor and detect propaganda in the tasks 7 and 11 of the SemEval-2020 competition. The use of UDA shows promising results with a systematic improvement of the performances over the four different subtasks, and even outperforms supervised learning with the additional labels of the Funlines dataset."
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<abstract>In this work, we combine the state-of-the-art BERT architecture with the semi-supervised learning technique UDA in order to exploit unlabeled raw data to assess humor and detect propaganda in the tasks 7 and 11 of the SemEval-2020 competition. The use of UDA shows promising results with a systematic improvement of the performances over the four different subtasks, and even outperforms supervised learning with the additional labels of the Funlines dataset.</abstract>
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%0 Conference Proceedings
%T WMD at SemEval-2020 Tasks 7 and 11: Assessing Humor and Propaganda Using Unsupervised Data Augmentation
%A Daval-Frerot, Guillaume
%A Weis, Yannick
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F daval-frerot-weis-2020-wmd
%X In this work, we combine the state-of-the-art BERT architecture with the semi-supervised learning technique UDA in order to exploit unlabeled raw data to assess humor and detect propaganda in the tasks 7 and 11 of the SemEval-2020 competition. The use of UDA shows promising results with a systematic improvement of the performances over the four different subtasks, and even outperforms supervised learning with the additional labels of the Funlines dataset.
%R 10.18653/v1/2020.semeval-1.246
%U https://aclanthology.org/2020.semeval-1.246/
%U https://doi.org/10.18653/v1/2020.semeval-1.246
%P 1865-1874
Markdown (Informal)
[WMD at SemEval-2020 Tasks 7 and 11: Assessing Humor and Propaganda Using Unsupervised Data Augmentation](https://aclanthology.org/2020.semeval-1.246/) (Daval-Frerot & Weis, SemEval 2020)
ACL